Quadratic Approximation for Log-Likelihood Ratio Processes

Gushchin, A.; Valkeila, E.

Авторы: Alexander Gushchin, Esko Valkeila
Сборник: Modern problems of stochastic analysis and statistics - selected contributions in honor of Valentin Konakov
Серия: Springer Series in Mathematics and Statistics
Том: 208
Год издания: 2017
Издательство: Springer Verlag
Местоположение издательства: Germany
Первая страница: 179
Последняя страница: 215
DOI: 10.1007/978-3-319-65313-6_8
Аннотация: We consider a sequence of general filtered statistical models with a finite-dimensional parameter. It is tacitly assumed that a proper rescaling of the parameter space is already done (so we deal with a local parameter) and also time rescaling is done if necessary. Our first and main purpose is to give sufficient conditions for the existence of certain uniform in time linear–quadratic approximations of log-likelihood ratio processes. Second, we prove general theorems establishing LAN, LAMN and LAQ properties for these models based on these linear–quadratic approximations. Our third purpose is to prove three theorems related to the necessity of the conditions in our main result. These theorems assert that these conditions are necessarily satisfied if (1) an approximation of a much more general form exists and a (necessary) condition of asymptotic negligibility of jumps of likelihood ratio processes holds, or (2) we have LAN property at every moment of time and the limiting models are continuous in time, or (3) we have LAN property, Hellinger processes are asymptotically degenerate at the terminal times, and the condition of asymptotic negligibility of jumps holds.
Добавил в систему: Гущин Александр Александрович

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Quadratic Approximation for Log-Likelihood Ratio Processesстатья